# Contributing

For bug fixes or new features, please file an issue before submitting a pull request.
If the change is not trivial, it may be best to wait for feedback.

<!-- ## Running tests

Tests are written as usual Python unit tests with the `unittest` module of the
Python standard library. Running them can be done as follow:

```console
$ python -m unittest discover -vv
``` -->


## Generating data

The `Makefile` in the project repository handles the generation of the
training and benchmarking datasets. Simply running `make` will re-generate
the `mibig-2.0` and `mibig-3.1` datasets, including the `features.hdf5` 
and `classes.hdf5` needed to train CHAMOIS.


## Managing versions

As it is a good project management practice, we should follow
[semantic versioning](https://semver.org/), so remember the following:

* As long as the model predicts the same thing, retraining/updating the model
  should be considered a non-breaking change, so you should bump the MINOR
  version of the program.
* Upgrading the internal HMMs could potentially change the output but won't
  break the program, they should be treated as non-breaking change, so you
  should bump the MINOR version of the program.
* If the model changes prediction (e.g. predicted classes change), then you
  should bump the MAJOR version of the program as it it a breaking change.
* Changes in the code should be treated following semver the usual way.
* Changed in the CLI should be treated as changed in the API (e.g. a new
  CLI option or a new command bumps the MINOR version, removal of an option
  bumps the MAJOR version).


## Upgrading the internal model

After having trained a new version of the model, simply copy the 
resulting JSON file to `chamois/predictor/predictor.json`. 


## Upgrading the internal HMMs

To download the subset of Pfam HMMs required by the model, simply run:

```console
$ python setup.py clean download_pfam --inplace
```